Global Payroll Performance Optimisation - II

1. David Kurtz Go-Faster Consultancy Ltd. david.kurtz@go-faster.co.uk www.go-faster.co.uk Global Payroll Performance Optimisation - II

2. Oracle Database Specialist Independent consultant Performance tuning PeopleSoft ERP Oracle RDBMS Book www.psftdba.com UKOUG Director Server Tech & PeopleSoft Oak Table Who Am I? ©2011 www.go-faster.co.uk

3. Agenda • ‘Streaming’ –Parallel processing • Data Volume • Read Consistency • Partitioning • Reporting • Archiving ©2011 www.go-faster.co.uk

4. Warning • This is an unashamedly technical session. • I am going to talk about database internals. ©2011 www.go-faster.co.uk

5. Size matters! ©2011 www.go-faster.co.uk

6. All modern machines have multiple processors, most of the processors have multiple cores. Even the CPU in my 4 year old laptop has a 2 core CPU. Parallel processing ©2011 www.go-faster.co.uk

7. Database Parallelism • All objects in the PeopleSoft schema are explicitly set NOPARALLEL • Indexes are built parallel, but later reset. • Can invoke parallel query with PARALLEL hint • Parallel insert in direct path model • Parallel DML only works on partitioned objects • 1 PQ slave per partition ©2011 www.go-faster.co.uk

8. PeopleSoft Batch Programs • Only run on one CPU at any one time. • Client Server processes • Program (COBOL or Application Engine) • Database (eg. Oracle) • Either busy executing COBOL or waiting for the database. • If your payroll calculation is a single process you are not getting value for money! ©2011 www.go-faster.co.uk

9. Payroll ‘Streaming’ • Several GP processes can be split up. • Each piece processes a distinct set of employees • Range of EMPLID • The pieces can be run concurrently. • Maximum number of streams determined by hardware. ©2011 www.go-faster.co.uk

10. COBOL Payroll Calculation Application Engine Banking Preparation GL Preparation EDI Preparation Payslip Preparation Database Intensive Streamable Processes ©2011 www.go-faster.co.uk

11. Payroll ‘Streaming’ Challenges • Payroll isn’t over until the last stream completes. • Streams need to be evenly balanced. • Employee churn? • One global definition of streams • Balance for largest payroll? • Inter-stream contention • Shared working storage tables in COBOL ©2011 www.go-faster.co.uk

12. Payroll Calculation Process Phases • Identify • Populate working storage and some result tables • Database Intensive • Calculation • COBOL Intensive • Cancellation • Delete results • Database Intensive ©2011 www.go-faster.co.uk

13. How Many Streams? • In a well tuned systems, the payroll calculation phase spends about • 2/3 of its time in COBOL • 1/3 on the database. • Number of streams should not exceed • 3 * CPU on database server • 1.5 * CPU on Process Scheduler server • Payroll identification process is database intensive. ©2011 www.go-faster.co.uk

14. How Many Streams? • First GP I ever worked on • 20 CPUs on Application/Batch server • 20 CPUs on Database server • Maximum number of streams? • 20 / 1/3 = 60 on database server • 20 / 2/3 = 30 on Application server • So we used 30 streams • Application server fully utilised during payroll calc • Database about 50% during calc, • Probably overloaded during identification. ©2011 www.go-faster.co.uk

15. How Many Streams? • Optimise number of streams for calculation phase. • Restrict concurrency of database intensive process on process scheduler. • To limit CPU consumption, and possibly also I/O contention. • Consider use of Oracle Resource Manager • Mainly for Payroll identification • I’ve never had to do this myself. • Cancellation will be restricted by I/O ©2011 www.go-faster.co.uk

16. Balancing Streams • Balance employees across streams on basis of • 80% number of payroll segments per stream • 20% number of JOB history rows • Longer serving employees in earlier streams likely to have more payroll segment and job history. • Make allowance for employee churn. • You will need to periodically rebalance the streams. • Balance for the largest payroll. ©2011 www.go-faster.co.uk

17. Employee Churn • EMPLID is allocated as an accession number. • Streams are a range of EMPLIDs • New employees are hired into the last stream • Employees are terminated across all streams • Over time the streams will go out of balance • Last stream will take longest • Periodically rebalance the streams ©2011 www.go-faster.co.uk

18. Bulk Churn Effects • Migration • If migrated to GP in tranches then order of migration could affect stream balance • Company merger/divestment history can affect balance of payroll. ©2011 www.go-faster.co.uk

19. Rebalancing the streams? • Calculate new stream range values • Allow space for estimated future growth • Rebuild all range partitioned tables • Half the I/O of partition merge/split • About 42 tables in UK tables. • Need working storage space to do this ©2011 www.go-faster.co.uk

20. Reversing the EMPLID • Reverse the EMPLID • Instead of EMPLID 0000012345 • Use EMPLID 543210000 • Streams stay balanced because new employees hired across range • Improved search performance across HCM • BUT you must do this before you go live! ©2011 www.go-faster.co.uk

21. 0000012345 0000012346 0000012347 0000012348 0000012349 0000012350 0000012351 0000012352 5432100000 6432100000 7432100000 8432100000 9432100000 0532100000 1532100000 2532100000 Reversing the EMPLID ©2011 www.go-faster.co.uk

22. Inter-stream Contention • Streams are just ranges of EMPLIDs. • Oracle inserts data into the first available block (roughly speaking) • Multiple streams insert data simultaneously into the same data blocks in result tables. • Payroll cancel/recalculation deletes from result tables. • Multiple transactions concurrently update different rows in the same block. • On Oracle/SQL Server >=2005: No locking, streams continue to run, but read consistency processing is expensive • Other database can experience page level locking ©2011 www.go-faster.co.uk

23. COBOL One shared instance of each working storage table Shared SQL Candidate for Global Temporary Table so one instance per session Application Engine PeopleSoft Temporary Record One instance of record per process Different SQL Still consider GTT to reduce redo Working Storage Tables ©2011 www.go-faster.co.uk

24. Read Consistency • The data set that you query remains the same throughout the life of your query. • If somebody else updates data that you are reading (and commits), after your query starts, then you see the original value. • Thus, readers do not block writers or vice versa. • Oracle has always done this, like this since 1990. • SQL Server 2005 has ‘read committed snapshot’ option • Other databases either block or can permit ‘dirty read’. ©2011 www.go-faster.co.uk

25. Read Consistency • Oracle achieves this by storing ‘undo’ information for every change • Recovers ‘read-consistent’ in-memory copy of data block to point in time when query started. • A good reason for buying Oracle • Resource intensive process • Performance problem if abused. • Global Payroll is the perfect storm! ©2011 www.go-faster.co.uk

26. Query @ 10023 Update @ 10024 Read Consistency ©2011 www.go-faster.co.uk

27. Avoiding Inter-stream Contention • Prevent different streams accessing the same data blocks • Range Partition result tables to match stream ranges • Use Global Temporary Tables (Oracle) for working storage tables • Partition these also on other platforms. • Now different streams access different partitions. • No code change, a job for the DBA • licensed option on most platforms ©2011 www.go-faster.co.uk

28. Partitioning • Partitioned Table • Different physical components • Value of data determines physical location • Logically still one table • Transparent to application • Rather like a multi-part encyclopaedia. • Partition Elimination ©2011 www.go-faster.co.uk

29. What is Partitioning? • Typically used in DSS • But can also be effective in OLTP • (From Oracle documentation) ©2011 www.go-faster.co.uk

30. Keep similar things together Employees for one stream in on partition Keep different things apart Only one transaction in each block of each segment No need for read consistency Partitioning ©2011 www.go-faster.co.uk

31. Partitioning GP Recommendation • Range Partitioning • EMPLID – to match streams • List Sub-partition • CAL_RUN_ID – calendar group ID. ©2011 www.go-faster.co.uk

32. Secondary Benefits • CAL_RUN_ID list sub-partition • Easier to archive later • Historical partitions • Different Tablespaces • Different Data Files • Old data on slower disk • Read Only • Less frequent back-up of read-only tables • Faster Backup ©2011 www.go-faster.co.uk

33. Global because the data is private Temporary because the definition is permanent Global because everyone can see the definition Temporary because physical existance of the table is temporary so it does not need to be recovered. Global Temporary Tables ©2011 www.go-faster.co.uk

34. Global Temporary Tables • A temporary object • No redo generation • But there is undo, and there is redo on the undo! • Each session gets its own physical copy. • Again no read consistency problems • No high water mark issues • Lower high water marks – less I/O ©2011 www.go-faster.co.uk

35. Building the DDL • Demonstrate GFCBUILD utility. ©2011 www.go-faster.co.uk

36. Group Lists • Specify a list of individual EMPLIDs for whom to run pay calc or another process. • Some customers have experienced problems when run groups shortly before or during larger batch payroll calculations. • Why? ©2011 www.go-faster.co.uk

37. Cost Based Optimizer • SQL Execution Plan Caching • Bind Variable Peeking during Parse • Different Plan for Group List • Because different bind variables • But plan cached and gets used for main pay calculation which then runs longer than usual! ©2011 www.go-faster.co.uk

38. Plan Stability • Remember the good plan used by large payroll. • Force it to be used for all payrolls including group list. • Data Volumes small so poor plan won’t really matter. • Oracle Stored Outline • No code change, DBA can implement. ©2011 www.go-faster.co.uk

39. Plan Stability • Collect and applied stored outline with database trigger • http://www.go-faster.co.uk/gpdoc.htm#gp.stored_outlines • Use Active Session History to demonstrate the problem and solution ©2011 www.go-faster.co.uk

40. Capture Stored Outline CREATE OR REPLACE TRIGGER sysadm.gfc_create_stored_outlines BEFORE UPDATE OF runstatus ON sysadm.psprcsrqst FOR EACH ROW WHEN (new.prcsname = 'GPPDPRUN' AND (new.runstatus = 7 OR old.runstatus = 7)) DECLARE l_sql VARCHAR2(100); BEGIN l_sql := 'ALTER SESSION SET create_stored_outlines = '; IF :new.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||:new.prcsname; ELSIF :old.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||'FALSE'; END IF; --because I dont want to crash the process scheduler EXCEPTION WHEN OTHERS THEN NULL; END; / ©2011 www.go-faster.co.uk

41. Apply Stored Outline CREATE OR REPLACE TRIGGER sysadm.gfc_use_stored_outlines BEFORE UPDATE OF runstatus ON sysadm.psprcsrqst FOR EACH ROW WHEN (new.prcsname = 'GPPDPRUN' AND (new.runstatus = 7 OR old.runstatus = 7)) DECLARE l_sql VARCHAR2(100); BEGIN l_sql := 'ALTER SESSION SET use_stored_outlines = '; IF :new.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||:new.prcsname; ELSIF :old.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||'FALSE'; END IF; --because I dont want to crash the process scheduler EXCEPTION WHEN OTHERS THEN NULL; END; / ©2011 www.go-faster.co.uk

42. Three Scenarios ComparedLarge / Small / Plan Stable Small SQL_ID SCENARIO 1 ASH_SECS SCENARIO 2 ASH_SECS SCENARIO 3 ASH_SECS ------------- ------------------- ---------- ------------ ---------- ------------ ---------- 4uzmzh74rdrnz 2514155560 280 3829487612 28750 **SAME** 5023 4n482cm7r9qyn 1595742310 680 869376931 140 **SAME** 889 2f66y2u54ru1v 1145975676 630 **SAME** 531 1n2dfvb3jrn2m 1293172177 150 **SAME** 150 652y9682bqqvp 3325291917 30 **SAME** 110 d8gxmqp2zydta 1716202706 10 678016679 10 **SAME** 32 2np47twhd5nga 3496258537 10 **SAME** 27 4ru0618dswz3y 2621940820 10 539127764 22 4ru0618dswz3y 539127764 100 **SAME** 22 4ru0618dswz3y 3325291917 10 539127764 22 4ru0618dswz3y 1403673054 110 539127764 22 gnnu2hfkjm2yd 1559321680 80 **SAME** 19 fxz4z38pybu3x 1478656524 30 4036143672 18 2xkjjwvmyf99c 1393004311 20 **SAME** 18 a05wrd51zy3kj 2641254321 10 **SAME** 15 ©2011 www.go-faster.co.uk

43. Data Volume • Payroll generates a lot of data. • Every pay period it generates more data. • Partitioning can offer ways of accessing the data you want quickly • Without having to trawl through data you don’t want. • Need to consider how long you need data • Do you still need data from last tax year? ©2011 www.go-faster.co.uk

44. Archiving • Put the data you do need to keep into a reporting table • Remove data from the live result tables • Partitioning can help you move/delete this data efficiently • May need to rebuild tables where you have to use DELETE • Reduced data volumes should improve performance of reports. ©2011 www.go-faster.co.uk

45. Reporting • Payroll result tables delivered with single index • Not suitably indexed for all reporting requirements • Particularly single PIN queries • Adding more indexes would degrade calculation performance • Consider generating reporting table • Subset of data, and indexed as necessary. ©2011 www.go-faster.co.uk

46. GFC_GPRPTGEN • Reporting Table for Single Pin Queries • List Partitioned by Pin • One Partition for each Pin • Incremental Maintenance by Application Engine • Uses Parallel DML to maintain reporting table. • Sub-Paritioned GP Result Tables may still be faster for single employee, single calendar group ID queries! ©2011 www.go-faster.co.uk

47. Further Reading • Configuring and Operating Streamed Processing in PeopleSoft Global Payroll • www.go-faster.co.uk/gpdocs.htm#Configuring_Operating_Streamed_Payroll • Managing Oracle Table Partitioning in PeopleSoft Applications with GFC_PSPART Package • www.go-faster.co.uk/gpdocs.htm#Managing_Oracle_Table_Partitioning • Use of Oracle Plan Stability (Stored Outlines) in PeopleSoft Global Payroll • www.go-faster.co.uk/gpdocs.htm#gp.stored.outlines ©2011 www.go-faster.co.uk

48. Questions?